Benchmarking Pretrained Models for Speech Emotion Recognition: A Focus on XceptionPublished in Computers

"Benchmarking Pretrained Models for Speech Emotion Recognition: A Focus on Xception," explores the performance of pretrained deep learning models in the field of Speech Emotion Recognition (SER). The authors emphasize the Xception architecture, benchmarking it against other models to evaluate its effectiveness in identifying and classifying emotional states from speech data. The study provides insights into model accuracy, efficiency, and practical applications of SER systems, which are essential for areas like human-computer interaction, customer service, and emotional well-being monitoring. This work contributes to improving SER technologies by leveraging state-of-the-art pretrained models.